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Michael A. Grasso, MD, PhD

Academic Title:

Assistant Professor

Primary Appointment:

Medicine

Secondary Appointment(s):

Emergency Medicine

Location:

110 S Paca, 6th Fl., Suite 200

Phone (Primary):

(410) 328-8025

Education and Training

  • University of Maryland, BS. Microbiology, 1983
  • American University, MS, Computer Science, 1986
  • University of Maryland Baltimore County, PhD, Computer Science, 1997
  • George Washington University School of Medicine & Health Sciences, MD, 2005
  • Residency, University of Maryland School of Medicine, Internal Medicine, 2008
  • Fellowship (Practice Pathway), University of Maryland School of Medicine, Department of Emergency Medicine, Clinical Informatics, 2013 

Biosketch

Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine. He practices Emergency Medicine through the University of Maryland School of Medicine.  He is also board certified in Clinical Informatics, Director of the Clinical Informatics Group at the University of Maryland School of Medicine, and  Program Director of the Graduate Certificate and Master of Science in Clinical Informatics programs at the University of Maryland Baltimore.

He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland Baltimore County. He completed residency training at the University of Maryland School of Medicine. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, the William Beaumont Medical Research Honor Society, is a Fellow of the American College of Physicians (FACP), and is a Fellow of the American Medical Informatics Association (FAMIA).

He has been awarded more than $2,000,000 in grant and contract funding from the National Institutes of Health, the Food and Drug Administration, the National Institute of Standards and Technology, the National Aeronautics and Space Administration, and the Department of Defense. He has authored more than 70 refereed publications, and has more than 25 years of experience in Clinical Informatics and Scientific Computing with an emphasis on software engineering, clinical decision support, and clinical data mining. He is currently working with the national clinical repository from the Veterans Health Administration, which contains data on more than 35 million patients from roughly 150 medical centers and 800 outpatient clinics. He also works with the EPIC clinical repository from the 14 member hospitals within the University of Maryland Medical System and the Maryland Emergency Medicine Network. His research focuses on knowledge representation and reasoning, quality improvement in Emergency Medicine, opioid prescribing practices, and online consumer health information.

Research/Clinical Keywords

Biomedical Informatics, Clinical Informatics, Consumer Health Informatics, Big Data Analytics, Data Mining, Predictive Modeling, Clinical Decision Support, Chronic Disease Management, Emergency Medicine, Internal Medicine, Safety and Quality, Pain Management, Opioid Abuse

Highlighted Publications

Grasso MA, Dezman ZD, Jerrard DA. Coding Disparity and Specificity during Emergency Department Visits after Transitioning to the Tenth Version of the International Classification of Disease. AMIA Annu Symp Proc. 2022 Nov 5-9.

Grasso MA, Rogalski A, Farrukh N, Kim Z, Nosrati B. Physician Prescribing Changes Impacted by Patient-Initiated Online Health Searches. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021 Dec:2682-2685, doi: 10.1109/BIBM52615.2021.9669731.

Mativo I, Yesha Y, Grasso MA, Oates T, Zhu Q. Hybrid Mortality Prediction using Multiple Source Systems. International Journal on Cybernetics & Informatics (IJCI). 2019 Feb;8(1):1-8.

Janeja VP, Gholap J, Walkikar P, Yesha Y, Rishe N, Niskar A, Grasso MA. Collaborative data mining in text analytics. Intelligent Data Analysis. 2018;22(3):491-513.

Grasso MA, Grasso CT, Jerrard DA. Prescriptions written for opioid pain medication in the Veterans Health Administration between 2000 and 2016. J Addict Med. 2017 Nov/Dec;11(6):483-488.

Grasso MA, Dezman ZD, Grasso CT, Jerrard DA. Opioid pain medication prescriptions obtained through emergency medical visits in the Veterans Health Administration. Journal of Opioid Management. 2017;13(2):77-84.

Grasso MA, Dezman ZD, Comer AC, Jerrard DA. The decline in hydrocodone/acetaminophen prescriptions in emergency departments in the Veterans Health Administration between 2009 to 2015. West J Emerg Med. 2016;17(4):396-403. Available online at http://escholarship.org/uc/item/29d2w30f/.

Grasso MA, Comer AC, DiRenzo DD, Yesha Y, Rishe ND. Using big data to evaluate the association between periodontal disease and rheumatoid arthritis. AMIA Annu Symp Proc. 2015 Nov 14-18; San Francisco, CA.

Payne E, Carlisle A, Desai S, Grasso MA. Using "Big Data" analytics and health care informatics to advance personalized health. APHA 143rd Annual Meeting and Expo. 2015 Oct 31-Nov 4; Chicago, IL.

Grasso MA, Cotter B, Jerrard DA. The impact of a follow-up clinic on unscheduled return emergency department visits. American College of Emergency Medicine, Research Forum. 2015 Oct 26-27; Boston, MA.

Grasso MA, Lemkin DL, Bond MC. Subspecialty training in clinical informatics: Prerequisite activities for potential applicants. EM Resident. 2015 Feb;42(1). http://www.emresident.org/subspecialty-training-in-clinical-informatics/.

Bochare A, Gangopadhyay A, Yesha Ye, Joshi A, Yesha Ya, Grasso MA, Brady M, Rishe N. Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer. International J of Medical Engineering and Informatics, 2014;6(2):87-99.

Grasso MA, Dalvi D, Das S, Gately M, Korolev V, Yesha Y. Genetic information for chronic disease prediction. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). 2011 Nov;:997.

Additional Publication Citations

A complete list of publications can be found at NCBI.

Research Interests

 

Research Interests

I am conducting research into innovative applications of biomedical informatics that expand the scope of clinical medicine, have a strong theoretical basis in computer science, and are of strategic importance to the University of Maryland Medical System. I am developing new approaches to knowledge representation and reasoning, which are optimized for very large clinical repositories, and which can be applied to disease prediction, critical event prediction, and treatment efficacy prediction. The clinical focus for this work includes several chronic diseases, opioid misuse and addiction, quality improvement in Emergency Medicine, opioid prescribing practices, and online consumer health information.

Current Research Projects

  • Knowledge Representation and Reasoning with Big Data - I am developing new methods for knowledge representation and reasoning that are optimized for very large clinical repositories, and which can be correlated with genomic and environmental data. My specific approach is to enhance machine learning algorithms with semantic analysis, domain information, and deep learning. My clinical focus is on coronary artery disease, diabetes, rheumatoid arthritis, chronic kidney disease, chronic pain, addiction, and mental illness. This work may lead to new approaches for disease prediction, critical event prediction, and treatment efficacy prediction. I am currently working with the national clinical repository from the Veterans Health Administration, which contains data on more than 35 million patients from roughly 150 medical centers and 800 outpatient clinics, as well as the EPIC clinical repository from the 14 member hospitals within the University of Maryland Medical System and the Maryland Emergency Medicine Network.

  • Patient Safety and Quality Measures in Emergency Medicine - Patient safety and quality is one of the nation's most important health care challenges. This is especially important in the emergency department, where health care teams are challenged to rapidly diagnose and treat multiple patients, some of whom present with potentially life-threatening illness. I am conducting research in resource utilization and recidivism in emergency medicine, with a focus on co-morbidities, key risk factors, adverse drug events, chronic pain, suicidality, addiction, utilization patterns, clinical workflow, SARS-CoV-2, opioid prescribing practices, and consumer health information.

  • Online Consumer Health Information - We know from prior studies that roughly one third of people search the internet for consumer health information before presenting to an emergency department for acute care, and that 75% of those searches contain inaccurate information. I am conducting research to examine the impact of these consumer health searches on the provision of patient care, with a focus on mismatched patient-physician expectations and changes in established practice guidelines.

Links of Interest